Developing FT-NIR and PLS1 Methodology for Predicting Adulteration in Representative Varieties/Blends of Extra Virgin Olive Oils

It was previously demonstrated that Fourier transform near infrared (FT‐NIR) spectroscopy and partial least squares (PLS1) were successfully used to assess whether an olive oil was extra virgin, and if adulterated, with which type of vegetable oil and by how much using previously developed PLS1 calibration models. This last prediction required an initial set of four PLS1 calibration models that were based on gravimetrically prepared mixtures of a specific variety of extra virgin olive oil (EVOO) spiked with adulterants. The current study was undertaken after obtaining a range of EVOO varieties grown in different countries. It was found that all the different types of EVOO varieties investigated belonged to four distinct groups, and each required the development of additional sets of specific PLS1 calibration models to ensure that they can be used to predict low concentrations of vegetable oils high in linoleic, oleic, or palmitic acid, and/or refined olive oil. These four distinct sets of PLS1 calibration models were required to cover the range of EVOO varieties with a linoleic acid content from 1.3 to 15.5 % of total fatty acids. An FT‐NIR library was established with 66 EVOO products obtained from California and Europe. The quality and/or purity of EVOO were assessed by determining the FT‐NIR Index, a measure of the volatile content of EVOO. The use of these PLS1 calibration models made it possible to predict the authenticity of EVOO and the identity and quantity of potential adulterant oils in minutes.     

Determination of the SiH content of hydrogen silicone oil by a combination of the fourier transform near infrared,attenuated total reflectance–fourier transform infrared,and partial least squares regression models

To verify the feasibility of the determination of the Si?H content (HC) of hydrogen silicone oil (HS‐oil) with Fourier transform near infrared (FT‐NIR) spectroscopy and attenuated total reflectance (ATR)–Fourier transform infrared (FTIR) spectroscopy combined with the partial least squares regression (PLS‐R) model, HS‐oil samples were synthesized from concentrated hydrosilicone oil (HC = 1.4 wt %), octamethylcyclotetrasiloxane, and hexamethyldisiloxane or prepared by the dilution of concentrated hydrosilicone oil with octamethylcyclotetrasiloxane. The FT‐NIR PLS‐R model (8695–4000 cm^?1, two principal components) was developed from the FT‐NIR spectral data, and the coefficient of determination for cross‐validation (R²) and the coefficient of determination for external validation (r²) were 0.992 and 0.995, respectively. The ATR–FTIR PLS‐R model (2302–2040 cm^?1, one principal component) was developed from the ATR–FTIR spectral data; it produced an R² of 0.995 and an r² of 0.996. This study demonstrated that the combination of FT‐NIR and ATR–FTIR spectroscopy with the PLS‐R model were successfully used to determine the HC of the HS‐oil. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40694.     

The Use of FTIR Spectroscopy and Chemometrics for Rapid Authentication of Extra Virgin Olive Oil

The authenticity of high value edible fats and oils including extra virgin olive oil (EVOO) is an emerging issue, currently. The potential employment of Fourier transform infrared (FTIR) spectroscopy in combination with chemometrics of multivariate calibration and discriminant analysis has been exploited for rapid authentication of EVOO from canola oil (Ca‐O). The optimization of two calibration models of partial least square (PLS) and principle component regression was performed in order to quantify the level of Ca‐O in EVOO. The chemometrics of discriminant analysis (DA) was used for making the classification between pure EVOO and EVOO adulterated with Ca‐O. The individual oils and their blends were scanned on good contact with ZnSe crystals in horizontal attenuated total reflectance, as a sampling technique. The wavenumbers of 3,028–2,985 and 1,200–987 cm^?1 were used for quantification and classification of EVOO adulterated with Ca‐O. The results showed that PLS with normal FTIR spectra was well suited for quantitative analysis of Ca‐O with a value of the coefficient of determination (R²) > 0.99. The error, expressed as root mean square error of calibration obtained was relatively low, i.e. 0.108 % (v/v). DA can make the classification between pure EVOO and that adulterated with Ca‐O with one misclassified reported.     

Determination of the Si?H content of hydrogen silicone oil by a combination of the fourier transform near infrared,attenuated total reflectance–fourier transform infrared,and partial least squares regression models

To verify the feasibility of the determination of the Si? H content (HC) of hydrogen silicone oil (HS‐oil) with Fourier transform near infrared (FT‐NIR) spectroscopy and attenuated total reflectance (ATR)–Fourier transform infrared (FTIR) spectroscopy combined with the partial least squares regression (PLS‐R) model, HS‐oil samples were synthesized from concentrated hydrosilicone oil (HC = 1.4 wt %), octamethylcyclotetrasiloxane, and hexamethyldisiloxane or prepared by the dilution of concentrated hydrosilicone oil with octamethylcyclotetrasiloxane. The FT‐NIR PLS‐R model (8695–4000 cm^?1, two principal components) was developed from the FT‐NIR spectral data, and the coefficient of determination for cross‐validation (R²) and the coefficient of determination for external validation (r²) were 0.992 and 0.995, respectively. The ATR–FTIR PLS‐R model (2302–2040 cm^?1, one principal component) was developed from the ATR–FTIR spectral data; it produced an R² of 0.995 and an r² of 0.996. This study demonstrated that the combination of FT‐NIR and ATR–FTIR spectroscopy with the PLS‐R model were successfully used to determine the HC of the HS‐oil. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40694.     

In‐process monitoring in industrial olive mill by means of FT‐NIR

A total of 287 olive lots and 161 olive oil samples were analyzed for fat content, moisture and free acidity, using a Fourier transform near‐infrared (FT‐NIR) instrument located in an industrial mill. Samples having a wide range of both reference values and olive lot sizes (from <0.5 to >4 t) were collected at three industrial mill plants, located in the same Italian region, which utilize different technological equipment for virgin olive oil production. Olive paste spectra were acquired in diffuse reflectance, while oil samples were measured in transmission. Calibration models for oil content and moisture of olives as well as free acidity of virgin olive oils were developed using partial least squares (PLS) regression, first derivative and straight line subtraction. Results of calibration and validation of the PLS models selected were good. The PLS results indicate good similarity between data obtained from FT‐NIR and reference laboratory methods, allowing a rapid and less expensive screening analysis. Unfortunately, the correlation between the oil yield values recorded for all olive lots at the industrial mills and the oil content predicted by FT‐NIR was not satisfactory (R² = 0.605).     

Discrimination of various poly(propylene) copolymers and prediction of their ethylene content by near‐infrared and Raman spectroscopy in combination with chemometric methods

Near‐infrared (NIR) diffuse reflectance (DR) spectra and Fourier‐transform (FT) Raman spectra were measured for 12 kinds of block and random poly(propylene) (PP) copolymers with different ethylene content in pellets and powder states to propose calibration models that predict the ethylene content in PP and to deepen the understanding of the NIR and Raman spectra of PP. Band assignments were proposed based calculation of the second derivatives of the original spectra, analysis of loadings and regression coefficient plots of principal component analysis (PCA) and principal component regression (PCR) (predicting the ethylene content) models, and comparison of the NIR and Raman spectra of PP with those of linear low‐density polyethylene (LLDPE) with short branches. PCR and partial least squares (PLS) regression were applied to the second derivatives of the NIR spectra and the NIR spectra after multiplicative scatter correction (MSC) to develop the calibration models. After MSC treatment, the original spectra yield slightly better results for the standard error of prediction (SEP) than the second derivatives. A plot of regression coefficients for the PCR model shows peaks due to the CH₂ groups pointing upwards and those arising from the CH₃ groups pointing downwards, clearly separating the bands due to CH₃ and CH₂ groups. For the Raman data, MSC and normalization were applied to the original spectra, and then PCR and PLS regression were carried out to build the models. The PLS regression for the normalized spectra yields the best results for the correlation coefficient and the SEP. Raman bands at 1438, 1296, and 1164 cm^?1 play key roles in the prediction of the ethylene content in PP. The NIR chemometric evaluation of the data gave better results than those derived from the Raman spectra and chemometric analysis. Possible reasons for this observation are discussed. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 87: 616–625, 2003     

Application of a spectroscopic method to estimate the olive oil oxidative status

A rapid Fourier transformed infrared (FTIR) attenuated total reflectance (ATR) spectroscopic method coupled with partial least squares (PLS), was developed to estimate the oxidation degree of extra virgin olive oil (EVOO). The reference values of EVOO oxidation for the FTIR calibration were obtained by the specific absorptions at 232 and 270 nm, due to the presence of conjugated diene (CD) and conjugated triene (CT) groups, as monitored by the UV spectrophotometric determination. Specific washing procedures were applied to the EVOO to obtain EVOOP and EVOOTP samples, without phenolic compounds and without tocopherols and phenols, respectively. To obtain different oxidation degrees covering wide CD and CT ranges, EVOO, EVOOP, and EVOOTP samples were subjected to a forced oxidation at 60°C for 20 days and aliquots of the oils were daily analyzed. Regression of the FTIR/PLS‐predicted CD and CT of individual oxidized oils EVOO, EVOOP, EVOOTP, and all combined oils (EVOOALL) against UV–Visible reference values demonstrated the good quality of the models in terms of R² and RMSECV values. The results of the study indicated that a strong correlation existed between FTIR and UV–Visible peak intensities. Practical applications: The FTIR‐ATR method coupled with PLS elaboration was developed and applied to predict the oxidation degree of EVOO samples with considerable advantages in terms of simplicity, analysis time, and solvent consumption as compared to the standard method. Moreover, suitable adjustments of the equipment could permit a rapid control at‐line in oil sector.     

Differentiation of mixtures of monovarietal olive oils by mid‐infrared spectroscopy and chemometrics

Fourier transform infrared (FT‐IR) spectroscopy in combination with chemometric techniques has become a useful tool for authenticity determination of extra‐virgin olive oils. Spectroscopic analysis of monovarietal extra‐virgin olive oils obtained from three different olive cultivars (Erkence, Ayvalik and Nizip) and mixtures (Erkence‐Nizip and Ayvalik‐Nizip) of monovarietal olive oils was performed with an FT‐IR spectrometer equipped with a ZnSe attenuated total reflection sample accessory and a deuterated tri‐glycine sulfate detector. Using spectral data, principal component analysis successfully classified each cultivar and differentiated the mixtures from pure monovarietal oils. Quantification of two different monovarietal oil mixtures (2–20%) is achieved using partial least square (PLS) regression models. Correlation coefficients (R²) of the proposed PLS regression models are 0.94 and 0.96 for the Erkence‐Nizip and Ayvalik‐Nizip mixtures, respectively. Cross‐validation was applied to check the goodness of fit for the PLS regression models, and R² of the cross‐validation was determined as 0.84 and 0.91, respectively, for the two mixtures.     

Clarifications of the Carbonyl and Water Absorptions in Fourier Transform Near Infrared Spectra from Extra Virgin Olive Oil

The Fourier transform near infrared (FT-NIR) spectrum of extra virgin olive oils (EVOO) shows two minor carbonyl absorptions at 5280 and 5180 cm^–1 that has been used to assess their authenticity. To establish components absorbing at 5280 cm^–1, volatile aldehydes and ketones, triacylglycerol (TAG), diacylglycerols (DAG), free fatty acids (FFA), phenolics, and water are investigated and sometimes added to refined olive oil (ROO). Except TAG, the remaining carbonyls contribute to 5280 cm^–1 by broadening peak. Water absorption is demonstrated by its removal using Na₂SO₄ or deuterium oxide addition; FT-NIR spectral changes are reconstituted by water addition. Water absorption depends on being free or complexed with polar compounds in oil. The size of absorption is not related to abundance, but on unique absorption specificity of components; water shows the strongest absorption. Heat removes water and volatiles, leaving behind DAG, FFA, and phenolics, and makes it possible to differentiate absorption of water, volatile and non-volatile carbonyls. Cloudy olive oils are analyzed using FT-NIR methodology after warming for 3 min at 50 °C. FT-NIR index values are replaced by a new calibration model based on correlating gravimetric mass loss of water plus volatiles with spectral changes. The FT-NIR methodology is expanded to include EVOOs with 15.5% to 21% linoleic acid. Practical Applications: Testing for authenticity of EVOOs remains a challenge because adulterations continue to be a problem due to economic gains. Spectroscopy methods, specifically FT-NIR, are much preferred to targeted chemical methods because they measure all constituents in products and are non-destructive and fast. The current universal FT-NIR methodology assesses 13 different parameters: five major FAs, and the DAG and FFA contents. The FT-NIR index value measuring the content of moisture plus volatiles is now replaced by a gravimetric determination. The methodology identifies four major types of adulterants, high in oleic acid, linoleic acid, palm olein or ROO. The composition of olive oils makes it necessary to develop five oil-specific groups, but cloudy samples still need to be clarified by slight warming before measuring. The value of this universal FT-NIR methodology will increase after being adopted by commercial and in regulatory settings.     

Measurement of soybean fatty acids by near-infrared spectroscopy: Linear and nonlinear calibration methods

A key element of successful development of new soybean cultivars is availability of inexpensive and rapid methods for measurement of FA in seeds. Published research demonstrated applicability of NIR spectroscopy for FA profiling in oilseeds. The objectives of this study were to investigate the applicability of NIR spectroscopy for measurement of FA in whole soybeans and compare performance of calibration methods. Equations were developed using partial least squares (PLS), artificial neural networks (ANN), and support vector machines (SVM) regression methods. Validation results demonstrated that (i) equations for total saturates had the highest predictive ability (r ²=0.91–0.94) and were usable for quality assurance applications, (ii) palmitic acid models (r ²=0.80–0.84) were usable for certain research applications, and (iii) equations for stearic (r ²=0.49–0.68), oleic (r ²=0.76–0.81), linoleic (r ²=0.73–0.76), and linolenic (r ²=0.67–0.74) acids could be used for sample screening. The SVM models produced significantly more accurate predictions than those developed with PLS. ANN calibrations were not different from the other two methods. Reduction in the number of calibration samples reduced predictive ability of all equations. The rate of performance degradation of SVM models with sample reduction was the lowest.     

Analysis of Cod-Liver Oil Adulteration Using Fourier Transform Infrared (FTIR) Spectroscopy

Analysis of the adulteration of cod-liver oil with much cheaper oil-like animal fats has become attractive in recent years. This study highlights an application of Fourier transform infrared (FTIR) spectroscopy as a nondestructive and fast technique for the determination of adulterants in cod-liver oil. Attenuated total reflectance measurements were made on pure cod-liver oil and cod-liver oil adulterated with different concentrations of lard (0.5–50% v/v in cod-liver oil). A chemometrics partial least squares (PLS) calibration model was developed for quantitative measurement of the adulterant. Discriminant analysis method was used to classify cod-liver oil samples from common animal fats (beef, chicken, mutton, and lard) based on their infrared spectra. Discriminant analysis carried out using seven principal components was able to classify the samples as pure or adulterated cod-liver oil based on their FTIR spectra at the selected fingerprint regions (1,500–1,030 cm⁻¹).     

Chemometric Studies on zNose™ and Machine Vision Technologies for Discrimination of Commercial Extra Virgin Olive Oils

The aim of this study was to classify Turkish commercial extra virgin olive oil (EVOO) samples according to geographical origins by using surface acoustic wave sensing electronic nose (zNose?) and machine vision system (MVS) analyses in combination with chemometric approaches. EVOO samples obtained from north and south Aegean region were used in the study. The data analyses were performed with principal component analysis class models, partial least squares‐discriminant analysis (PLS‐DA) and hierarchical cluster analysis (HCA). Based on the zNose? analysis, it was found that EVOO aroma profiles could be discriminated successfully according to geographical origin of the samples with the aid of the PLS‐DA method. Color analysis was conducted as an additional sensory quality parameter that is preferred by the consumers. The results of HCA and PLS‐DA methods demonstrated that color measurement alone was not an effective discriminative factor for classification of EVOO. However, PLS‐DA and HCA methods provided clear differentiation among the EVOO samples in terms of electronic nose and color measurements. This study is significant from the point of evaluating the potential of zNose? in combination with MVS as a rapid method for the classification of geographically different EVOO produced in industry.     

Multivariate determination of cloud point in palm oil using partial least squares and principal component regression based on FTIR spectroscopy

A rapid FTIR spectroscopic method was developed for quantitative determination of the cloud point (CP) in palm oil samples. Calibration samples were prepared by blending randomized amounts of palm olein and palm stearin to produce a wide range of CP values ranging between 8.3 and 47.9°C. Both partial least squares (PLS) and principal component regression (PCR) calibration models for predicting CP were developed by using the FTIR spectral regions from 3000 to 2800 and 1800 to 1600 cm⁻¹. The prediction capabilities of these calibration models were evaluated by comparing their standard errors of prediction (SEP) in an independent prediction set consisting of 14 palm oil samples. The optimal model based on PLS in the spectral range 1800-1600 cm⁻¹ produced lower SEP values (2.03°C) than those found with the PCR (2.31°C) method. FTIR in conjunction with PLS and PCR models was found to be a useful analytical tool for simple and rapid quantitative determination of CP in palm oil.     

Simultaneous determination of molecular weight and crystallinity of recycled HDPE by infrared spectroscopy and multivariate calibration

An attempt of correlating molecular weight (M_n) of recycled high‐density polyethylene (HDPE) as measured by size‐exclusion chromatography (SEC) with diffuse reflectance near and mid‐infrared spectroscopy (NIR/MIR) was made by means of multivariate calibration. The spectral data obtained was also used to extract information about the degree of crystallinity of the recycled resin. Differential scanning calorimetry (DSC) was used as the reference method. Partial least‐squares (PLS) calibration was performed on the MIR and NIR spectral data for prediction of M_n. Four PC factors described fully the PLS models. The root‐mean‐square error of prediction (RMSEP) obtained with MIR data was 360, whereas a RMSEP of 470 was achieved when calibration was carried out on the diffuse reflectance NIR data. A PLS calibration for prediction of degree of crystallinity was performed on the NIR data in the 1100–1900‐nm region, but the ability of prediction of this model was poor. However a PLS calibration in the region 2000–2500 nm yield better results. Four PC factors explained the most of the variance in the spectra and the RMSEP was 0.4 wt %. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 321–327, 2002     

Multivariate calibration in Fourier transform infrared spectrometry as a tool to detect adulterations in Brazilian gasoline

In the present work, Fourier transform infrared spectroscopy (FTIR) in association with multivariate chemometrics classification techniques was employed to identify gasoline samples adulterated with diesel oil, kerosene, turpentine spirit or thinner. Results indicated that partial least squares (PLS) models based on infrared spectra were proven suitable as practical analytical methods for predicting adulterant content in gasoline in the volume fraction range from 0% to 50%. The results obtained by PLS provided prediction errors lower than 2% (v/v) for all adulterant determined. Additionally, Soft Independent Modeling of Class Analogy (SIMCA) was performed using all spectral data (650-3700 cm⁻¹) for sample classification into adulterant classes defined by training set and the results indicated that undoubted adulteration detection was possible but identification of the adulterant was subject to misclassification errors, specially for kerosene and turpentine adulterated samples, and must be carefully examined. Quality control and police laboratories for gasoline analysis should employ the proposed methods for rapid screening analysis for qualitative monitoring purposes.     

Infrared Spectroscopy and Multivariate Calibration for Quantification of Soybean Oil as Adulterant in Biodiesel Fuels

This work quantifies the adulteration of ethyl and methyl soybean biodiesels/diesel (B5) blended with soybean oil using mid‐infrared spectroscopy associated with multivariate calibration. The models constructed by the method of partial least squares (PLS) presented low values of root‐mean‐square error of prediction 0.22 % (w/w) and 0.26 % (w/w), respectively, for models containing ethyl and methyl soybean biodiesel. Along with the parameters of error, accuracy was evaluated by the use of an elliptical joint confidence region (EJCR). The EJCR for the both PLS models showed there was no significant difference between the prepared concentration values and PLS predicted concentration values, and that there was no evidence of bias within the 95 % confidence level. The PLS models showed excellent correlation in the prediction set (R = 0.999) and did not present systematic errors according to the ASTM E1655 standard. Therefore, the models presented excellent performance in quantifying soybean oil as an adulterant in B5 blends, in concentrations within the range 1.00–30.00 % (w/w). The proposed methodology showed itself to be efficient for quality control of B5 contaminated with vegetable oil.     

Assessment of resin formulations and determination of the formaldehyde to urea molar ratio by near‐ and mid‐infrared spectroscopy and multivariate data analysis

The molar ratios of formaldehyde (F) to urea (U) of three resin formulations in the range from 0.90 to 1.49 have been analyzed by means of Attenuated Total Reflection‐Fourier Transform Infrared (ATR‐FTIR) spectroscopy and Fourier Transform‐Near‐Infrared (FT‐NIR) spectroscopy. Application of Principal Component Analysis (PCA) to the spectra (MIR and NIR) allowed to separate them according to the molar ratio and to distinguish between two groups of resins. Soft Independent Modeling of Class Analogy (SIMCA) allowed classification of new resin samples with high model distances between the classes. Partial Least Squares Regression (PLS‐R) models based on MIR (NIR) spectra resulted in high coefficients of determination (R²) values, low errors, and high residual prediction deviations (RPD). To confirm the reproducibility of the process and to carefully evaluate twice all multivariate analysis results obtained, different batches of resins have been prepared to have an additional independent sample set. The number of samples required for MIR and possible applications of MIR and NIR spectroscopy in this context including limitations have been discussed. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2013     

Detection of Extra Virgin Olive Oil Adulteration With Edible Oils Using Front‐Face Fluorescence and Visible Spectroscopies

Synchronous front‐face fluorescence and visible spectroscopies are utilized for the simple, rapid, and nondestructive quantification of extra virgin olive oil (EVOO) adulteration with corn, soybean, and sunflower oils. For each adulterant, 42 adulterated EVOO samples in the adulterant amount in the range 1.0–50 g/100 g were prepared. The partial‐least‐squares regression was executed for quantification. Both full (leave‐one‐out) cross‐validation and external validation were performed to evaluate the predictive ability. The plots of observed vs. predicted values exhibit high linearity. The coefficient of determination (R²) values are larger than 0.99. The root mean square errors of both cross‐validation and prediction are no more than 2%. The detection limits for the three seed oils using fluorescence and visible spectroscopies are in the range of 1.5–2.2% and 1.8–2.4%, respectively. The merit of this method is that both the front‐face fluorescence and visible spectroscopies are recorded toward neat oils, avoiding any sample pretreatment including dilution.     

Mid‐infrared spectroscopy as a tool for real‐time monitoring of ethanol absorption in glycols

Fast, simple, accurate, and inexpensive methods for obtaining analyte concentration data are desirable in the industrial sector. In the present study, the use of Fourier transform mid‐infrared (FT‐MIR) spectroscopy, combined with partial least squares (PLS) regression, was investigated as a tool for real‐time monitoring of processes of ethanol absorption in glycols. Calibration was performed using simple synthetic samples containing ethanol, water, and monoethylene glycol (MEG) or diethylene glycol (DEG). The PLS models presented excellent performance, with correlation coefficients (R²) close to unity and root‐mean‐square errors of cross‐validation (RMSECV) and prediction (RMSEP) lower than 2% of the calibration data ranges for both analytes (ethanol and water) in both absorbents (MEG and DEG). The monitoring technique developed has potential to be applied in absorption processes and could also be used in other large‐scale unit operations, providing information in real time and enhancing process control.     

Detection of Olive Oil Adulteration Using FT-IR Spectroscopy and PLS with Variable Importance of Projection (VIP) Scores

Determination of adulteration and authenticity of extra virgin olive oil (EVOO) was investigated by means of infrared spectroscopy and chemometric methods. The study was focused on the detection and quantification of extra virgin olive oil adulteration by soybean (SB) and sunflower (SF) oils using FT-IR spectroscopy based on the use of PLS modeling and variable importance of projection (VIP) scores. A PLS model, using orthogonal signal correction and mean centering data pretreatments, and VIP scores variable preselection, was able to predict the concentration of sunflower and soybean oil adulterants in the 1–24 % weight ratio range with relative prediction errors lower than 3 % (w/w), for external validation samples. Moreover, the PLS-DA (discriminant analysis) model using the same preselected wavelengths was able to explain 99.9 % of variance and to predict with 100 % accuracy both classes of adulteration (EVOO–SB and EVOO–SF) in the external validation.